Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy. While estimating the scene flow from LiDAR has progressed recently, it remains largely unknown how to estimate the scene flow from a 4-D radar - an increasingly popular automotive sensor for its robustness against adverse weather and lighting conditions. Compared with the LiDAR point clouds, radar data are drastically sparser, noisier and in much lower resolution. Annotated datasets for radar scene flow are also in absence and costly to acquire in the real world. These factors jointly pose the radar scene flow estimation as a challenging problem. This work aims to address the above challenges and estimate scene flow from 4-D radar point clouds by leveraging self-supervised learning. A robust scene flow estimation architecture and three novel losses are bespoken designed to cope with intractable radar data. Real-world experimental results validate that our method is able to robustly estimate the radar scene flow in the wild and effectively supports the downstream task of motion segmentation.
翻译:光流使得自动飞行器能够了解多个独立物体的任意移动,这是长期移动自主的关键。 估计来自LiDAR的场景流动最近有所进展, 但对于如何从四维雷达(一种日益受欢迎的汽车感应器)对场景进行预测,以抵御恶劣天气和照明条件。 与LiDAR点云相比,雷达数据非常稀疏、噪音更强、分辨率更低。 在现实世界中,雷达场景流动的附加数据集也缺乏而且成本更高。 这些因素共同构成雷达场景流动估计这一具有挑战性的问题。 这项工作的目的是利用自我监督的学习,应对上述挑战,并估计四维雷达点云的场景流动。 一个强有力的场景流量估计架构和三个新的损失是设计用来应对难解的雷达数据。 现实世界实验结果证实,我们的方法能够稳健地估计野外雷达场流动,并有效地支持下游任务。